At a glanceSummarised by Seekless from the posting.
Must have8
Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience
5+ years of experience in data engineering, within a cloud environment
Proficiency in SQL
Strong programming skills in Python, Rust, or Java
Experience building and maintaining data pipelines using processing/streaming frameworks (e.g., Kafka, Flink, Beam, Spark) and orchestration tools (e.g., Airflow)
Experience architecting data stores and schemas for AI workflows (e.g., RAG)
Active Google Cloud certifications, or willingness to obtain within one month of joining
Builder mentality and bias for action
Nice to have5
Deep expertise in the Google Cloud Platform (GCP) ecosystem, specifically building streaming and batch pipelines using Dataflow (Apache Beam), Pub/Sub, BigQuery, and Cloud Composer (Airflow)
Strong background in data modeling and architecture across relational (e.g., PostgreSQL), NoSQL (e.g., Firestore, MongoDB), and graph databases (e.g., Neo4j), including modern cloud data warehouses (e.g., BigQuery) and data lakes (e.g., GCS, Dataproc)
Demonstrated experience setting up infrastructure for modern data science, machine learning, or Generative AI (e.g., preparing unstructured data, vector databases, RAG pipelines)
Familiarity with regulatory compliance frameworks (FedRAMP, HIPAA, etc.) and security strategies
Experience with modern data platforms like Snowflake or Databricks
Eligibility1
US Citizen
Skills
SQL
Python
Rust
Java
Kafka
Flink
Beam
Spark
Airflow
GCP
Dataflow
Apache Beam
Pub/Sub
BigQuery
Cloud Composer
PostgreSQL
Firestore
MongoDB
Neo4j
GCS
Dataproc
Snowflake
Databricks
About the company
"SZNS Solutions (pronounced “seasons”) is a technology consulting company and Google Cloud Partner based in Reston VA. We specialize in delivering agentic AI and cloud computing solutions. Founded by ex-Googlers with engineers from Google, Amazon, and Capital One, SZNS differentiates itself particularly in AI, data engineering, blockchain, and cloud-native software application development.”
Role Summary
The Data Engineer is the bedrock of intelligence operations, responsible for turning raw, unstructured data into actionable intelligence. You will embed directly with clients to build the data pipelines that power AI workflows. We build systems that don’t just move data, but do it with the speed and reliability required for live and automated intelligence. Ultimately, you’ll create the foundations that will influence key strategic decisions for us and our customers.
Key Responsibilities
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ETL/ELT Pipelines: Architect and deploy pipelines to ingest, transform, and store data from high volume, disparate sources for real-time analysis
•
Build for the Enterprise: Create a highly reliable single source of truth for enterprise intelligence and enablement
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AI Workflow Enablement: Architect and optimize production-grade data foundations to support high-performance AI workflows and automated decision-making.
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Operations & Governance: Establish and automate strict data security, quality assurance, and governance processes. Design systems for high fault tolerance and rapid disaster recovery
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Efficiency: Design and model for efficient queries, resource usage, workload scheduling, and cost
Requirements
Minimum Qualifications
•
Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience
•
5+ years of experience in data engineering, within a cloud environment, demonstrating a clear progression from engineering into architectural design
•
Proficiency in SQL and strong programming skills in Python, Rust, or Java
•
Experience building and maintaining data pipelines using processing/streaming frameworks (e.g., Kafka, Flink, Beam, Spark) and orchestration tools (e.g., Airflow)
•
Experience architecting data stores and schemas for AI workflows (e.g., RAG)
•
Active Google Cloud certifications, or willingness to obtain within one month of joining
•
Builder mentality and bias for action
•
US Citizen
Preferred Qualifications
Nice to have
•
Deep expertise in the Google Cloud Platform (GCP) ecosystem, specifically building streaming and batch pipelines using Dataflow (Apache Beam), Pub/Sub, BigQuery, and Cloud Composer (Airflow)
•
Strong background in data modeling and architecture across relational (e.g., PostgreSQL), NoSQL (e.g., Firestore, MongoDB), and graph databases (e.g., Neo4j), including modern cloud data warehouses (e.g., BigQuery) and data lakes (e.g., GCS, Dataproc)
•
Demonstrated experience setting up infrastructure for modern data science, machine learning, or Generative AI (e.g., preparing unstructured data, vector databases, RAG pipelines)
•
Familiarity with regulatory compliance frameworks (FedRAMP, HIPAA, etc.) and security strategies
•
Experience with modern data platforms like Snowflake or Databricks
Benefits
•
Competitive salary and benefits package
•
Hybrid work environment (MWF in-person in our Reston office)